How Many Rays Do I Need for Monte Carlo Optimization? ?W'z5'|
While it is important to ensure that a sufficient number of rays are traced to F;Q,cg M
distinguish the merit function value from the noise floor, it is often not necessary to d<-f:}^k0
trace as many rays during optimization as you might to obtain a given level of akv i^]x
accuracy for analysis purposes. What matters during optimization is that the g`pq* D
changes the optimizer makes to the model affect the merit function in the same way h,{Q %sqO
that the overall performance is affected. It is possible to define the merit function so mI8EeM a{
that it has less accuracy and/or coarser mesh resolution than meshes used for 8$NVVw]2,
analysis and yet produce improvements during optimization, especially in the early =2&\<Q_Fi
stages of a design. SWr TM
A rule of thumb for the first Monte Carlo run on a system is to have an average of at +@C hZ
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays Xz4q^XJ
on the receiver to achieve uniform distribution. It is likely that you will need to & ZD@-"@
define more rays than 800 in a simulation in order to get 800 rays on the receiver. FQ>$Ps*a[
When using simplified meshes as merit functions, you should check the before and k3bQ32()
after performance of a design to verify that the changes correlate to the changes of WX4sTxJK
the merit function during optimization. As a design reaches its final performance e bze_:
level, you will have to add rays to the simulation to reduce the noise floor so that #}#m\=0
sufficient accuracy and mesh resolution are available for the optimizer to find the b/_Zw^DPC
best solution.
